Machine learned curating of videos for selection and display

ABSTRACT

Techniques for video manipulation based on machine learned video curating are disclosed. Web page content is loaded, where the content includes a frame for short-form videos. The content of the web page is analyzed for textual information. A short-form video server is accessed. Short-form videos are selected from the short-form video server, where the selecting includes automatically curating the short-form videos. Adaptive learning is used for the selecting, based on a user&#39;s web page behavior. The adaptive learning includes collecting the user&#39;s web page behavior before the selecting. Automatic curating includes selecting, by a neural network, a subset of short-form videos appropriate for the web page. The web page frame is populated with the short-form videos obtained from the video server. Representations of the short-form videos are displayed within the frame on the web page. The short-form videos are auto played within the frame.

RELATED APPLICATIONS

This application claims the benefit of U.S. provisional patentapplications “Machine Learned Curating of Videos for Selection andDisplay” Ser. No. 62/970,364, filed Feb. 5, 2020, and “Machine LearnedVideo Template Usage” Ser. No. 63/086,077, filed Oct. 1, 2020.

Each of the foregoing applications is hereby incorporated by referencein its entirety.

FIELD OF ART

This application relates generally to video manipulation and moreparticularly to machine learned curating of videos for selection anddisplay.

BACKGROUND

One very popular digital pastime is “web surfing”, where web surfing hascome to refer generally to spending productive, leisure, andprocrastination time on the Internet. In fact, people love web surfing.While the web surfing can include meaningful activities such asresearch, education, or work, more often than not, the web surfing isundertaken for shopping, entertainment, gaming, looking for adorablevideos of children or pets, or just killing time. People use a widerange of electronic devices for web surfing in order to engage with theplethora of online information and content found at various websites. Toget to a particular website, a person starts a web browser on her or hiselectronic device and navigates to the website by typing in a webaddress or uniform resource locator (URL). The URL refers to a specificdigital address, essentially the digital equivalent of a physicaladdress. The URL takes the person to a home or landing page. The websitehomepage presents a variety of content that includes news, sports,politics, adorable puppy videos, kittens doing whacky things videos,products or services for sale, and much, much more. The person can clickon stories, sports scores, conspiracy theories, or whatever content isof interest to her or him. From a user perspective, finding aninteresting website typically starts either by navigating to a familiarsite or with a web search. Whichever technique is used, the user has tohave a destination or topic in mind to begin their web surfingexperience.

Alternately described as “The Wild West”, or a library with its entirecollection strewn across the floor, the web poses significant searchchallenges when it comes to finding particular content. In order toassist in the locating of desired content from among the severalbillions of websites online, search engines have been developed. Oneenters a search string such as, “cute puppy videos”, or “small formfactor computer” into the search engine and initiates the search.However, such general search strings yield vast numbers of hits—nearly300 billion for cute puppies alone. An ineffective search yields far toomany results to be useful or meaningful to the person initiating thesearch. While writing a concise search string would suggest a solutionto this problem, too concise a search can eliminate “near matches”,which might actually be closer to what the person seeks. From a providerperspective, such as that of an online retailer, success or failure oftheir business relies on presenting goods and services to prospectivecustomers and then converting the prospective customers to buyers. The“right” web page needs to be presented quickly to the potential customerlooking for a good or service. If such a presentation is not made, thecustomer will go elsewhere or will simply give up due to loss ofinterest or lack of time.

Web page creators create their web pages using search engineoptimization (SEO) techniques that increase the rankings of their webpages and improve the chances that their web pages will be presentedfirst to users searching the web. Search engine providers examine theweb pages that use the SEO techniques to rank the web pages in thesearch results. These ranked search results are presented to the user inthe hopes of directing that user to the web page where she or he willmake a purchase. SEO is a bit like a game of cat and mouse. The creatorstry to create web pages that will be more highly ranked, while thesearch engine developers try to determine whether the higher rankingsare legitimate or gamed. Clearly deceptive and underhanded techniques,such as automating access to a specific page to increase its hit rateand thus its apparent popularity are strongly discouraged. However,legitimate approaches to search engine optimization are encouraged. Thislatter category includes guides for creating web pages such as embeddingmeta-titles, descriptors, and keywords within the web page code;structuring URLs so that they can be easily followed by a search engine;and tagging images with appropriate keywords; among other actions. Whendone properly, web pages created using SEO techniques successfully risein web page rankings and attract users to the web pages.

SUMMARY

Electronic devices such as desktop computers, laptop computers, tablets,smartphones, and PDAs, are widely used by people who want to observe andinteract with web content. The web content, which is often renderedwithin web browsers as web pages, presents news, government information,entertainment, educational material, and so on. The web contenttypically includes text, videos including live-feed video, audio, andthe like. An individual interacting with the web page may choose tolearn more about a news story, a sports team, a product or service, etc.Seeking further information can include conducting a web search, whichcan result in hundreds, thousands, or more search hits. The individualmust then decide whether to try out some of the search results, conductfurther searches, etc. If the search involves seeking the latest gossipon a celebrity or tracking a breaking news story, the number andrelevance of the top search results can be small and quite manageable,allowing the user to choose her or his preferred information source. Bycontrast, if the search is for a product or service, and the individualis bombarded with too many results of low relevance, then the individualis highly unlikely to “convert” or buy the good or service. To controlthe number and the quality of the search results, curating the searchresults and selecting a few results which are highly relevant to theindividual's information quest can be far more effective. The individualis quickly presented with a reasonable number of relevant choices, cansafely select the top result, choose their favorite source, and so on.Further, the quality and efficiency of the curating the selection anddisplay of the search results can be significantly improved by applyingartificial intelligence (AI) techniques such as adaptive learning. Byobserving the individual as she or he behaves while interacting with aweb page, the selection of the top search results can be greatlyenhanced, thus refining the relevance of results, improving theindividual's user experience (UX), and increasing the probability of theindividual “converting” from a viewer to a purchaser or consumer.

Video manipulation is based on machine learned curating of videos forselection and display. A processor-implemented method for videomanipulation is disclosed comprising: loading content of a web page,wherein the content includes a frame for a plurality of short-formvideos; analyzing the content of the web page for textual information;accessing a short-form video server; selecting a plurality of short-formvideos from the short-form video server based on the textualinformation, wherein the selecting includes automatically curating theplurality of short-form videos; populating the frame on the web pagewith the plurality of short-form videos obtained from the short-formvideo server; and displaying representations of the plurality ofshort-form videos within the frame on the web page. The automaticcurating can include using a neural network, such as a recurrent neuralnetwork, to select a subset of the plurality of short-form videos thatare appropriate for the web page. The neural network can implement amodel such as a long short-term memory model, where the long short-termmemory model uses feedback within the neural network to processsequences of data. The sequences of data can include speech or videos.The automatic curating can be based on machine learning such as deeplearning. The machine learning can include training the neural networkfor the automatic curating by applying training data to the neuralnetwork. The short-form videos can be auto played within the frame onthe web page. A response to a call to action embedded within the frameon the web page can be received. A second plurality of short-formvideos, based on the response to the call to action from the short-formvideo server, can be provided. The criteria for the second plurality ofshort-form videos can modify the automatic curating of the plurality ofshort-form videos.

The automatic curating can be enhanced or updated using adaptivelearning. Adaptive learning is a technique that can include collectinginformation associated with an individual, analyzing that information,and using the analysis results to update various parameters orinterconnections associated with the neural network. The updatingparameters associated with the neural network can include updatingweights, biases, coefficients, etc., associated with the neural network.The collected information that can be associated with the adaptivelearning can include the individual's web page behavior, swipes and/orclicks on a web page, comparison with other individuals' web pagebehavior, and the like. Contextual information from a web page anddemographic data associated with the individual can also be used foradaptive learning. The quality of the improvements made to the neuralnetwork increases as more data is collected and analyzed. The individualcan choose to “opt in” to enable collection of further informationassociated with usage or website behavior of the user on a givenwebsite, behavior on additional websites, and so on.

Various features, aspects, and advantages of various embodiments willbecome more apparent from the following further description.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description of certain embodiments may beunderstood by reference to the following figures wherein:

FIG. 1A is a flow diagram for machine learned curating of videos.

FIG. 1B is a flow diagram for displaying curated videos.

FIG. 2 is an example web page with a frame.

FIG. 3 shows embedded videos on a desktop/laptop device.

FIG. 4 illustrates embedded videos on a mobile device.

FIG. 5 is a block diagram for a web page communicating with a source.

FIG. 6 is a flow diagram for adaptive learning usage.

FIG. 7 is a system diagram for video manipulation.

DETAILED DESCRIPTION

Techniques are disclosed for video manipulation based on machine learnedcurating of videos for selection and display. Web pages can be loaded byan individual using an electronic device such as a desktop computer, alaptop computer, a tablet, a smartphone, a personal digital assistant(PDA), and so on. The web pages that can be loaded can include text,images, videos, audio, and other content that present a message, afeeling or visceral reaction, a theme, an experience, and so on, relatedto or associated with the web pages. While the information on a givenweb page may be useful, access to similar or related web pages is widelyknown to greatly enhance the user experience as the individual consumesthe web page content. For example, if an individual is consuming newsreports and wishes to learn more about a particular topic, a simplesearch can easily yield hundreds or thousands of search results or hits.The myriad results can include similar key words, topics, names, and soon, but the usefulness or merit of the vast proportion of the resultscan be questionable. A “smart search” can reduce the number of searchresults, but knowing how to do such an enhanced search can be timeconsuming and confusing. Curated selection of the search results greatlyenhances the experience of the individual with their selected web page.

Machine learned curating of videos for selection and display enablesvideo manipulation. Content of a web page is loaded, where the contentincludes a frame for a plurality of short-form videos. The content ofthe web page is analyzed for textual information. The content of the webpage can be further analyzed for video information, audio information,and so on. A short-form video server is accessed, where the short-formvideo server is decentralized from storage and content for the remainderof the web page. A plurality of short-form videos is selected from theshort-form video server based on the textual information, where theselecting includes automatically curating the plurality of short-formvideos. The automatic curating includes using a neural network to pick asubset of the plurality of short-form videos that are appropriate forthe web page. The frame on the web page is populated with the pluralityof short-form videos obtained from the short-form video server. Theframe can include a horizontally scrollable frame, a grid, and so on.Representations of the plurality of short-form videos are displayedwithin the frame on the web page. The plurality of short-form videos isplayed within the frame. A response to a call to action embedded withinthe frame on the web page is received. A second plurality of short-formvideos is provided, based on the response to the call to action from theshort-form video server, where criteria for the second plurality ofshort-form videos modifies the automatic curating of the plurality ofshort-form videos. As a user's web page behavior is observed, adaptivelearning can be used for selecting short-form videos. The adaptativelearning enables machine learned curating of videos for selection anddisplay. The adaptive learning can be based on a user's web pagebehavior. The adaptive learning can include collecting the user's webpage behavior before the selecting occurs, that is, prior web pagebehavior. The adaptive learning can include collecting the user's swipesand/or clicks on the web page or on web pages related to the web page.

FIG. 1A is a flow diagram for machine learned curating of videos.Videos, which can include short-form videos, can include various mediatypes such as news videos, entertainment videos, political messagevideos, and so on. A short-form video can include a video with aduration of a few seconds, a minute, etc. The videos can be viewed on anelectronic device used by an individual. The videos can be includedamong additional contents of a web page, where the additional contentscan include text and audio. The web page can include a frame. Theshort-form videos can be stored on a video server, where the videoserver is decentralized from the remainder of the web page content.Short-form videos can be selected from the server and used to populatethe frame on the web page being viewed by the individual. The frame thatis populated by the short-form videos can include a horizontalorientation, a vertical orientation, a grid, and the like. The frame canbe scrollable in the horizontal orientation, in the verticalorientation, and in the grid configuration. The short-form videos withinthe frame can be auto played.

The flow 100 includes loading content of a web page 110. The loading ofthe web page content can be accomplished using a web browser. The webpage can include a web page selected by an individual. The web page caninclude content from a variety of websites such as a news website, ane-commerce website, a government website, and so on. The web pagecontent can include text, images, video, audio, and the like. The textcan include fonts, characters, emojis, etc. The video and audio can bebased on a variety of standards or protocols such as MPEG-4™, Flash™,QuickTime™, etc. for video, and MPEG-3™, WAV™, AIFF™ etc. for audio. Thecontent of the web page that is loaded can be based on the electronicdevice used by the individual to view the contents of the web page. Theindividual can load the web page content using a desktop computer, alaptop computer, a tablet, a smartphone, a PDA, or other electronicdevice. The web page content that is loaded can be determined based ondisplay size, display resolution, download settings configured by theindividual, etc. In the flow 100, the content includes a frame 112 for aplurality of short-form videos. Discussed throughout, the frame can bescrollable and can be included in a horizontal orientation, a verticalorientation, or a grid orientation. In embodiments, the frame can beadded to an existing web page to facilitate display of relevantshort-form videos, as discussed below. The frame can be positionedwithin the web page, can include a floating box over the web page, etc.In further embodiments, the frame on the web page can pull a pluralityof short-form videos from a short-form video server. The short-formvideos can be displayed within the frame on the web page.

The flow 100 includes analyzing the content of the web page for textualinformation 120. The analyzing the textual information can includeprocessing the text for one or more keywords, one or more key phrases,and so on. Textual data can include the actual words on a web page thata user sees. Textual data can be a purer representation of web pagecontent than SEO metadata and the like. The analyzing can furtherinclude analysis of video information. The video information can includekeywords associated with the video; objects, people, or animals withinthe video; and the like. The analyzing can also include an analysis ofaudio information. The analyzing audio information can include naturallanguage processing. The flow 100 further includes augmenting thetextual information with metadata 130. Metadata, or “data about data”,can provide information in addition to the results of the analysis. Themetadata can include a timestamp, web journey information (e.g., pagevisits, buttons clicked), user information, SEO data, cookies, etc.Discussed below, the augmenting the textual information with metadatacan be used for selecting a plurality of short-form videos based on themetadata. The augmenting with metadata can also be applied to videoinformation and audio information. The flow 100 further includesaugmenting the textual information with image information 132 andselecting the plurality of short-form videos based on the imageinformation. The augmenting textual information with image informationcan include an image description, keyword, or phrase; a description ofimage contents; etc. The flow 100 further includes augmenting thetextual information with themes 134 extracted from the web page, whereinthe selecting of themes is based on ranking associations of metadatawith short-form video theme information. A web page theme can include alook, a style, a mood, or “feeling” communicated by a web page. Thetheme can include happy or sad, scary or motivational, calling to takean action, entertaining or annoying, etc. The flow 100 includesaccessing a short-form video server 140. A short-form video can includea video the duration of which includes a few seconds such as 15 seconds,a minute, and so on. The short-form video can include a video used formarketing, commercial, news, governmental, educational, or otherpurposes. The video server can include a local server, a remote server,a cloud-based server, a grid server, etc. The video server can include aserver that is decentralized from other compute and storage capabilitiesthat are involved in the web page content and rendering.

The flow 100 includes selecting a plurality of short-form videos 150from the short-form video server based on the textual information. Theplurality of short-form videos can include two or more videos, where thetwo or more videos can include substantially similar content. The shortform can provide a range of content that might by generally related,where the general relation can include “news”, “entertainment”,“travel”, and the like. In a usage example, the selection of short-formvideos could include travel videos of destinations as diverse as scubadiving in the Caribbean, trekking in the Himalayas, or bungee jumping inAustralia. In the flow 100, the selecting includes automaticallycurating 152 the plurality of short-form videos. The curating theselection of the short-form videos can be based on user preferences, amarketing message, and so on. In further embodiments, the automaticcurating can be based on a long short-term memory model which tracksrecent behavior by a user accessing the web page. The recent behavior bythe user can include web pages visited, menu selections made, buttonsclicked, etc. In embodiments, the curating can use temporally weightedbehaviors by a user accessing the web page. The temporally weightedbehaviors can include an amount of time spent by the user on a given webpage, an amount of time between actions such as button clicks orscrolling, and the like.

In the flow 100, the automatic curating includes selecting, by a neuralnetwork 154, a subset of the plurality of short-form videos that areappropriate for the web page. The neural network can include aconvolutional neural network. In embodiments, the neural network can bebased on a long short-term memory model, where the long short-termmemory model can be executed on a recurrent neural network. Other neuralnetwork models and configurations can be used. In embodiments, theautomatic curating can include machine learning. The machine learningcan be accomplished on a neural network such as a deep learning neuralnetwork. The machine learning can be accomplished using supervised,unsupervised, or semi-supervised techniques for training the neuralnetwork. The training the neural network can include using known-gooddata for which expected results have be determined by calculation,estimation, human experts, etc. In other embodiments, the automaticcurating provides the subset of videos based on contextual informationfrom the web page. The contextual information can include keywords,hidden text, tags, and the like, associated with the web page. Thecontextual information can include web page information such as whetherthe web page is a home page, a product page, a news story page, etc. Inother embodiments, the contextual information can be based on videoinformation from the web page. The video information can include type ofvideo, video contents, and the like.

The flow 100 includes using adaptive learning 156. The adaptive learningcan be based on a user's web page behavior. Discussed throughout,short-form videos can be selected based on criteria such as textualinformation. The selecting can further include automatic curation of theshort-form videos. As a user's web page behavior is observed, adaptivelearning can be used for selecting short-form videos. The adaptativelearning enables machine learned curating of videos for selection anddisplay. The adaptive learning can be based on a user's web pagebehavior. The adaptive learning can include collecting the user's webpage behavior before the selecting occurs, that is, prior web pagebehavior. The adaptive learning can include collecting the user's swipesand/or clicks on the web page or on web pages related to the web page.The adaptive learning can include comparing other users' historicalpatterns of web page behavior with the user's current web page behavior.The adaptive learning can combine the user's web page behavior withcontextual information from the web page. The adaptive learning caninclude demographic data about the user. An “opting-in” by the user canenable the collection of additional web page behavior about the user.The collecting of additional web page behavior can include monitoringthe user's web page behavior on additional websites. The user can berewarded for opting in.

Various steps in the flow 100 may be changed in order, repeated,omitted, or the like without departing from the disclosed concepts.Various embodiments of the flow 100 can be included in a computerprogram product embodied in a non-transitory computer readable mediumthat includes code executable by one or more processors.

FIG. 1B is a flow diagram for displaying curated videos. The flow 102includes populating the frame 160 on the web page with the plurality ofshort-form videos obtained from the short-form video server. Thepopulating can include obtaining the plurality of short-form videos fromthe server and loading the short-form videos into the frame on the webpage. The flow 102 includes displaying representations 170 of theplurality of short-form videos within the frame on the web page. Therepresentations can include reduced size or “thumbnail” versions of theshort-form videos. The full-size videos can be viewed by selecting orclicking on the representations of the videos. In embodiments, the framecan show representations of the plurality of short-form videos in alinear fashion. The frame can comprise a horizontal configuration or avertical configuration. The number of short-form videos that populatethe frame may not all be visible at one time. The frame can bescrollable. In other embodiments, the frame can show representations ofthe plurality of short-form videos in a grid fashion. The grid can alsobe scrollable.

The flow 102 further includes auto playing the plurality of short-formvideos 180 within the frame. The auto playing can occur once the frameis populated. The auto playing the short-form videos can include autoplaying videos that are visible within the frame. As scrolling occurswithin the frame, the short-form videos that become visible within theframe can be auto played. The auto playing can occur when the individualinteracting with the web page moves a cursor over the short-form videorepresentation within the frame. The flow 102 further includes receivinga response to a call to action 190 embedded within the frame on the webpage. A call to action can include urging the individual viewing the webpage to take some action. The call to action can include signing apetition, making a purchase, signing up for a newsletter, and so on. Theresponse to a call to action can include a button click, data enteredinto a webform, and the like. The flow 102 further includes providing asecond plurality of short-form videos 192, based on the response to thecall to action, from the short-form video server. Criteria for thesecond plurality of short-form videos can be used to modify theautomatic curating of the plurality of short-form videos. The secondplurality of short-form videos can include a subset of the firstplurality of short-form videos, an additional set of short-form videos,a different set of short-form videos, etc.

Various steps in the flow 102 may be changed in order, repeated,omitted, or the like without departing from the disclosed concepts.Various embodiments of the flow 102 can be included in a computerprogram product embodied in a non-transitory computer readable mediumthat includes code executable by one or more processors.

FIG. 2 is an example web page with a frame. A web page that includes aframe can be rendered on a display. The frame can be populated withshort-form videos which have been selected based on analysis of textualinformation analyzed within the web page. The contents of the frame areselected based on machine learned curating of videos for selection anddisplay. Content of a web page is loaded, where the content includes aframe for a plurality of short-form videos. The content of the web pageis analyzed for textual information. A short-form video server isaccessed. A plurality of short-form videos is selected from theshort-form video server based on the textual information, where theselecting includes automatically curating the plurality of short-formvideos. The frame on the web page is populated with the plurality ofshort-form videos obtained from the short-form video server.Representations of the plurality of short-form videos are displayedwithin the frame on the web page. Embodiments include auto playing theplurality of short-form videos within the frame.

An example web page with a frame is shown 200. The web page and framecan be rendered on a display 210, where the display can include adisplay associated with a computing device such as a laptop computer ordesktop computer; a personal electronic device such as a tablet,smartphone, or PDA; and so on. The web page can include web content 220.The web content can include text, images, video clips, videos, audio,audio clips, and so on. Discussed throughout, the web page content isanalyzed for textual information, video information, audio information,etc. A plurality of short-form videos is selected based on analysis ofthe information gleaned from the web page. The selected short-formvideos can be used to populate a frame 230 included in the web pagecontent. The short-form videos can include short-form video 1 240,short-form video 2 242, short-form video 3 244, short-form video 4 246,short-form video 5 248, and short-form video 6 250. While six short-formvideos are shown, other numbers of short-form videos can populate theframe on the web page.

FIG. 3 shows embedded videos on a desktop/laptop device 300. A varietyof web browsers can be used to display a web page on an electronicdevice such as a laptop computer, a desktop computer, and so on. The webpage that is displayed can include a frame that can be populated withshort-form videos selected from a plurality of short-form videosselected from a short-form video server. The short-form videos withinthe frame can be curated, where the curating is based on machinelearning related to videos selection.

Web pages and frames included within the web page can be displayed on auser device such as a laptop computer, a desktop computer, and so on.Given that a web page displayed on a laptop, for example, is renderedwith a horizontal orientation, the frame within the web page can also berendered with a horizontal orientation. Recall that a plurality ofshort-form videos is selected from a short-form video server, and thatthe frame within the web page is populated with the selected short-formvideos. To access the short-form videos represented within the frame,the user can scroll horizontally 310 within the frame. The horizontalscrolling within the frame can be supported while the user scrolls downthe web page. The horizontal scrolling frame remains visible even at thebottom of an article 312 or other web content of a web page. Otherconfiguration techniques can be used for the frame within the web page.In embodiments, a grid 314 can be used for displaying representations ofthe plurality of short-form videos selected from the short-form videoserver and used to populate the frame within the web page.

A video player can be used to play a short-form video or other videoselected by the user. The video player can include an MPEG-4™ player, aFlash™ player, a QuickTime™ player, and so on. The video player can beused to play the selected short-form video in various configurations. Inembodiments, the configuration in which the video player plays theshort-form video can include a vertical 320 or portrait configuration ororientation. The selected short-form video that is selected by the userfor play can be zoomed, scaled, cropped, etc., in order to fit withinthe vertical configuration. In other embodiments, the configuration forplaying the short-form video can include a horizontal 322 or landscapeconfiguration. The horizontal configuration can include various aspectratios including common aspect ratios such as 4:3, 16:9, and so on. Infurther embodiments, the configuration for playing the short-form videocan include a square 324 configuration. The square configuration can beaccomplished by zooming, scaling, cropping, etc. the short-form video.

FIG. 4 illustrates embedded videos on a mobile device. A mobile devicecan be used to access a web page on which a frame comprisingrepresentations of selected short-form videos can be displayed. Thedisplaying the short-form videos can be based on machine learnedcurating of videos for selection and display. Content of a web page isloaded. The content includes a frame for a plurality of short-formvideos. The content of the web page is analyzed for textual information.A short-form video server is accessed. A plurality of short-form videosis selected from the short-form video server based on the textualinformation. The selecting includes automatically curating the pluralityof short-form videos. The frame on the web page is populated with theplurality of short-form videos obtained from the short-form videoserver, and representations of the plurality of short-form videos aredisplayed within the frame on the web page.

Examples of embedded videos displayed on a mobile device are shown 400.Content of a web page, where the content includes a frame, can includehorizontal scrolling 410. The horizontal scrolling can includehorizontal scrolling among the videos selected from the short-form videoserver and displayed within the frame. Horizontal scrolling can includeswiping left, swiping right, etc., to find a desired short-form video.The short-form video can then be selected or played by tapping therepresentation of the short-form video. The frame that enableshorizontal scrolling among the representations of the selected videoscan persist irrespective of where on the web page the user isinteracting with web page content. In 412, the user has scrolled to theend of the web content on the web page. Note that the frame that enableshorizontal scrolling among the selected short-form videos is stilldisplayed. Other frame configurations can be included. In embodiments,the frame can display representations of the selected short-form videosin a grid 414. The grid can enable scrolling horizontally andvertically.

A selected short-form video can be viewed by the user by running a videoplayer. The video player can include a video player app that can beinstalled on the electronic device. The video player can play theselected short-form video in various orientations. The orientations caninclude orientations coded by a developer of the web page, anorientation of the electronic device, an orientation selected by a user,and so on. In embodiments, the video player can play the short-formvideo in a vertical orientation 420. The short-form video can be scaled,cropped, etc., to fit the vertical orientation. In other embodiments,the video player can play the short-form video in a horizontalorientation 422. The horizontal orientation can include various displayratios such as 4:3, 16:9, “letter box”, etc. As for the verticalorientation, the video player can scale, crop, adjust, and the like, theshort-form video for display in the horizontal orientation. In furtherembodiments, the video play can play the short-form video in a squareconfiguration 424. The short-form video can be scaled and cropped to fitthe short-form video into the square configuration. The squareconfiguration can be useful for maximizing the center of a short-formvideo for ease of viewing the short-form video.

FIG. 5 is a block diagram for a web page communicating with a source500. The communication between the web page and the source enablesmachine learned curating of videos for selection and display. Content ofa web page is loaded where the content includes a frame for a pluralityof short-form videos. The content of the web page is analyzed fortextual information, and a short-form video server is accessed. Aplurality of short-form videos is selected from the short-form videoserver based on the textual information where the selecting includesautomatically curating the plurality of short-form videos. The frame onthe web page is populated with the plurality of short-form videosobtained from the short-form video server. Representations of theplurality of short-form videos are displayed within the frame on the webpage.

Described throughout, short-form videos can be selected from a pluralityof short-form videos hosted by a short-form video server. The selectingcan be based on a web page visited by a user. The web page can includeone of a plurality of web pages that can be viewed by the user. Based onanalysis of the web page, short-form videos are selected from the serverand displayed on the web page viewed by a user. The user can visit a webpage using an electronic device 510. The electronic device can include adesktop or laptop computer, a tablet or smartphone, a personal digitalassistant (PDA), and so on. The electronic device is coupled to adisplay 512 on which a web page 514 can be rendered. A frame 516 on theweb page is populated with videos such as video 1 518, video N 520, andso on. While two videos are shown, other numbers of videos can bepopulated within the frame. The electronic device can be incommunication with a web page content detector 530. The communicationbetween the electronic device and the web page content detector can beaccomplished using a communication channel such as a wirelesscommunication channel 532. The web page content detector can analyze theweb page for textual information. The textual information can bedetected by searching for text on the web page. In addition, web pagetags, hidden text, and so on can augment the textual information. Thetextual information can be detected based on natural language analysisof audio data from the web page. The web page content detector can becontrolled by an artificial intelligence (AI) engine 540. The AI engine,which can be based on a neural network such as a convolutional neuralnetwork or a recurrent neural network, can be used to curate selectionor picking of one or more short-form videos. The short-form videos thatare selected can be used to populate the frame 516 within the web page514. The AI engine 540 can include adaptive learning 542. Adaptivelearning, described presently, can be embedded in the AI engine 540 orcan be implemented in a distributed fashion.

The web page content detector 530 can access a short-form video server550. The short-form video server can be decentralized from other computeand storage capabilities related to the web page. The short-form videoserver can be in communication with the electronic device via acommunications channel 552, where the communications channel can includea wireless communications channel. The short-form video server canprovide a plurality of short-form videos. The short-form videos can beselected from short-form video data storage 554. The short-form videodata storage can include decentralized storage. The short-form videosselected by the short-form video server can be used to populate theframe 516 on the electronic device 510. Representations of theshort-form videos can be displayed within the frame on the web page,where the displaying can include auto playing the short-form videos.

FIG. 6 is a flow diagram for adaptive learning usage. Discussedthroughout, short-form videos can be selected based on criteria such astextual information. The selecting can further include automaticcuration of the short-form videos. As a user's web page behavior isobserved, adaptive learning can be used for selecting short-form videos.The adaptative learning enables machine learned curating of videos forselection and display. Content of a web page is loaded, where thecontent includes a frame for a plurality of short-form videos. Thecontent of the web page is analyzed for textual information. Ashort-form video server is accessed. A plurality of short-form videos isselected from the short-form video server based on the textualinformation, where the selecting includes automatically curating theplurality of short-form videos. The frame on the web page is populatedwith the plurality of short-form videos obtained from the short-formvideo server. Representations of the plurality of short-form videos aredisplayed within the frame on the web page.

The flow 600 includes using adaptive learning 610 for the selecting. Theadaptive learning can be used to adjust the selecting of short-formvideos for a user. The adaptive learning can include selections made bythe user, websites visited, content viewed, and so on. The adaptivelearning can be based on preferences presented by the user, a useridentification (ID), and the like. In the flow 600, the adaptivelearning includes collecting the user's web page behavior 612 before theselecting. The user's web page behavior can include websites visited,menu items selected, radio buttons clicked, and so on. The websitebehavior can be used to infer user preferences. In a usage example,website behavior could be used to determine a user's preferences forshort-form videos containing dogs over cats; mountain landscapes overdesert islands, Baroque string quartets over death metal rages, and soon. The user behavior can be observed for adaptive learning prior to theselecting. The user behavior can be based on past use over an amount oftime such as an hour, a day, a week, time since subscribing to awebservice or enabling an app, and the like. The user behavior can bebased on content viewed, menu selections chosen, radio buttons pressed,etc., for a single page, for types of web pages such as news pages orentertainment pages, and so on. The user behavior can be based oncontent or selections across a plurality of web pages.

In the flow 600, the adaptive learning includes collecting the user'sswipes and/or clicks 620 on the web page. The one or more swipesexecuted by the user can include swiping up, down, left, or right;swiping in a clockwise or counterclockwise rotation motion; and so on.The one or more swipes can enable selection or deselection, approval ordisapproval, moving through a list such as a list of options, and thelike. The one or more clicks can include clicking on an object, a radiobutton, a menu selection, etc.; using a human digit touching or pressinga touch screen; using a mouse or a trackpad; and the like. In the flow600, the adaptive learning includes collecting the user's swipes and/orclicks on web pages related 622 to the web page. The web pages that canbe related to the web page can include web pages accessible through theweb page; web pages that provide similar content such as news sources,shopping sites, or social networks; web pages that provide similarshort-form videos such as cute puppy or kitten videos; anime musicvideos (AMVs); etc.

In the flow 600, the adaptive learning includes comparing 630 otherusers' historical patterns of web page behavior with the user's web pagebehavior. The comparison of the user's web page behavior with that ofother users' historical patterns can be useful to anticipating sitesthat the user might want to visit, content such as short-form videosrelated to the user's purpose for visiting a web page, and the like. Theother users may be friends of the user or otherwise associated with theuser, a selection of other users etc. In the flow 600, the adaptivelearning combines the user's web page behavior with contextualinformation 640 from the web page. The contextual information caninclude a portion or region of the web page with which the user isinteracting, a time of day, a day of week, etc. The contextualinformation can enable curation of videos such as short-form videos forselection and display. The contextual information can include currentevents, popular memes, and the like.

In the flow 600, the adaptive learning includes demographic data 650about the user. The demographic data can include information associatedwith the user such as age, gender or gender identity, race, ethnicity,religious affiliation if any, etc. The demographic data can includesocio-economic information such as employment status, educational level,income level, marital or domestic partnership status, and the like. Thedemographic data can further include more general data such as state,region, or country of residence. The demographic data can enableselection of videos based on appropriate curation such as selectingage-appropriate material, culturally-appropriate material, etc. The flow600 further includes an opting-in by the user 660 to collect additionalweb page behavior. The opting-in can be accomplished by enrolling,providing user credentials, clicking a button, checking a box, and soon. The opting-in can be used for collecting research data, enhancingthe user's experience, etc. In the flow 600, the additional web pagebehavior includes monitoring 662 the user's web page behavior onadditional websites. The additional websites can include websitesassociated with the website, websites coupled to or accessible to thewebsite, and the like. The additional websites can include websitesvisited randomly by the user. The flow 600 further includes rewardingthe user 664 for opting in. The rewarding of the user can includecompensating the user such as providing a discount on a first purchase;paying the user; assigning the user credits that can be used foraccessing videos such as short-form videos; crediting a blockchaincoupon; and so on.

FIG. 7 is a system diagram for video manipulation. Video manipulation isbased on machine learned curating of videos for selection and display.The system 700 can include one or more processors 710 attached to amemory 712 which stores instructions. The system 700 can include adisplay 714 coupled to the one or more processors 710 for displayingdata, videos, intermediate steps, instructions, short-form videos, andso on. In embodiments, one or more processors 710 are attached to thememory 712 where the one or more processors, when executing theinstructions which are stored, are configured to: load content of a webpage, wherein the content includes a frame for a plurality of short-formvideos; analyze the content of the web page for textual information;access a short-form video server; select a plurality of short-formvideos from the short-form video server based on the textualinformation, wherein the selecting includes automatically curating theplurality of short-form videos; populate the frame on the web page withthe plurality of short-form videos obtained from the short-form videoserver; and display representations of the plurality of short-formvideos within the frame on the web page.

The system 700 can include a collection of videos and data 720. Thevideos and data 720 may be stored in storage such as electronic storagecoupled to the one or more processors, a database, one or morestatically linked libraries, one or more dynamically linked libraries,or other appropriate video or data formats. The videos can includeshort-form videos. A short-form video can include a video that can beshown with an amount of time including a few seconds, several seconds, aminute, and so on. A short-form video can convey content quickly andefficiently to a viewer of the short-form video. The short-form videocan present a story, an advertisement, a political message, and thelike. A short-form video can include a video from among a plurality ofvideos, where the videos can comprise a wide range or variety ofcontent. The data can include textual information or data that can beassociated with a web page, as discussed below. The textual informationcan be augmented with image information, themes, and so on. The system700 can include a loading component 730. The loading component 730 caninclude functions and instructions for loading content of a web page.The content can include a frame such as a frame within the web page. Theframe can be used for a plurality of short-form videos. In embodiments,the frame can be added to an existing web page. Embodiments can includereceiving a response to a call to action embedded within the frame onthe web page. A call to action can include “click here”, “sign up now”,“buy now”, etc. The call to action can include registration, a financialactivity, a political action, and the like.

The system 700 can include an analyzing component 740. The analyzingcomponent 740 can include functions and instructions for analyzing thecontent of the web page for textual information. The textual informationcan include banner text, title text, content text, hidden text, and soon. The textual information can include text associated with images,videos, GIFs, etc. The textual information can include a sponsor name orinformation, web page ownership or responsibility names or information,and the like. The system 700 can include an accessing component 750. Theaccessing component 750 can include functions and instructions foraccessing a short-form video server. The short-form video server caninclude a local server, a cloud-based server, a mesh server, and so on.The short-form videos accessible through the video server can includeadvertising videos, social videos, news and information videos,political message videos, and so on. The short-form videos accessiblethrough the short-form video server can include videos in a variety ofvideo formats such as MPEG-4™, Flash™ QuickTime™, etc.

The system 700 can include a selecting component 760. The selectingcomponent 760 can include functions and instructions for selecting aplurality of short-form videos from the short-form video server based onthe textual information. The selecting includes automatically curatingthe plurality of short-form videos. The selecting can include selectingshort-form videos comprising substantially similar content,substantially dissimilar content, and so on. The selecting can includeadaptive learning of a user's web page behavior. Embodiments includeaugmenting the textual information with metadata and performing theselecting the plurality of short-form videos based on the metadata.Metadata, or “data about data”, can include a time of day, a day ofweek, or some other period of time. The metadata can be based oninferring demographic data about a user, obtaining data from a userprofile, determining web page history, and the like. Other embodimentsinclude augmenting the textual information with image information andperforming the selecting the plurality of short-form videos based on theimage information. The image information can include image contentinformation such as whether it contains an advertisement, entertainment,or a political message. The image content can include environmentalinformation such as urban or rural; developed or undeveloped; ocean ormountains; animals such as dogs, cats, or wild creatures; daytime ornighttime; etc. Further embodiments include augmenting the textualinformation with themes extracted from the web page, wherein theselecting is based on ranking associations of metadata with short-formvideo theme information. A web page theme can include a look, a style,or a “feeling” communicated by a web page. The theme can include happyor sad, scary or motivational, entertaining or annoying, etc. Themetadata from a given web page can be compared or associated withmetadata from one or more other web pages.

The automatic curating of the plurality of short-form video can beaccomplished by selecting short-form videos. The selecting theshort-form videos can be based on a “script”, predetermined policy, anadvertising campaign, a political message, and the like. In embodiments,the automatic curating comprises selecting, by a neural network, asubset of the plurality of short-form videos that are appropriate forthe web page. The neural network can be used to infer an appropriateselection of short-form videos, to predict an appropriate selection, andthe like. The neural network can include a convolutional neural network,a recurrent neural network, etc. In embodiments, the automatic curatingincludes machine learning. The machine learning can include training theneural network with a training dataset, where the training datasetincludes known good data and expected outcomes based on the data. Themachine learning can include deep learning, and can be based onunsupervised learning, supervised learning, etc. In other embodiments,the automatic curating can provide the subset based on contextualinformation from the web page. The contextual information from thewebsite can include the type of website such as a news website, anentertaining website, an e-commerce website, and the like. Inembodiments, the contextual information can be based on natural languageprocessing for audio information from the web page. The audioinformation can be audio information provided when a user first visitsthe website, audio information that results from the user navigating thewebsite based on menu selections or button clicks, etc. In furtherembodiments, the contextual information can be based on videoinformation from the web page. The video information can be provided bythe website, selected by the user, and so on. In other embodiments, theautomatic curating can be based on a long short-term memory model whichtracks recent behavior by a user accessing the web page. A longshort-term memory model can be implemented on a recurrent neuralnetwork. A long short-term memory model can process single data pointssuch as one or more images, sequences of data such as audio or video,etc. In embodiments, the curating uses temporally weighted behaviors bya user accessing the web page. The temporally weighted behaviors caninclude an amount of time spend on a web page, video, audio, etc. Thetemporally weighted behaviors can be based on a frequency of buttonclicks or menu selections, an amount of time between button clicks ormenu selections, and so on.

The system 700 can include a populating component 770. The populatingcomponent 770 can include functions and instructions for populating theframe on the web page with the plurality of short-form videos obtainedfrom the short-form video server. The frame can be configured in ahorizontal orientation, a vertical orientation, a matrix configuration,etc. In embodiments, the frame that is populated with the short-formvideos can be added to an existing web page to facilitate display ofrelevant short-form videos. The system 700 can include a displayingcomponent 780. The displaying component 780 can include functions andinstructions for displaying representations of the plurality ofshort-form videos within the frame on the web page. The displayingrepresentations can include resizing the short-form videos to fit withinthe web page width or height as configured by the user. The displayingrepresentations can be based on a device such as a laptop computer,tablet, or smartphone being used by the user. Embodiments include autoplaying the plurality of short-form videos within the frame. The autoplaying the short-form videos can include looping on a portion of theshort-form video, playing the entire video, etc. The displayingcomponent 780 can include an immersive viewing experience. Whileconventional video viewing refers to simply staring at a mobile devicedisplaying the short-form video, video viewing can be transformed intoan interactive and participatory experience. An immersive viewingexperience enables the user to rotate, move, and tilt the mobile deviceused to view the video while watching the video. As a result, such animmersive viewing experience is no longer exclusive to videos recordedusing panoramic/spherical videos or viewed using VR (virtual reality)devices. Specifically, a good/smooth viewing experience may be definedwhere the video stays in bound (the user cannot see outside the video,which results in an undesirable partial black screen), the user can zoomin only when necessary (to enable the user to view as much of the videoas possible), a stable view is provided (to avoid dramatic movement orscaling on rotating or tilting), a smart view is provided (when only apartial view of the video is visible, showing the significant part ifpossible); and video quality is ensured (in every angle/tilt ofviewing).

The system 700 can include a computer program product embodied in anon-transitory computer readable medium for video manipulation, thecomputer program product comprising code which causes one or moreprocessors to perform operations of: loading content of a web page,wherein the content includes a frame for a plurality of short-formvideos; analyzing the content of the web page for textual information;accessing a short-form video server; selecting a plurality of short-formvideos from the short-form video server based on the textualinformation, wherein the selecting includes automatically curating theplurality of short-form videos; populating the frame on the web pagewith the plurality of short-form videos obtained from the short-formvideo server; and displaying representations of the plurality ofshort-form videos within the frame on the web page.

Each of the above methods may be executed on one or more processors onone or more computer systems. Embodiments may include various forms ofdistributed computing, client/server computing, and cloud-basedcomputing. Further, it will be understood that the depicted steps orboxes contained in this disclosure's flow charts are solely illustrativeand explanatory. The steps may be modified, omitted, repeated, orre-ordered without departing from the scope of this disclosure. Further,each step may contain one or more sub-steps. While the foregoingdrawings and description set forth functional aspects of the disclosedsystems, no particular implementation or arrangement of software and/orhardware should be inferred from these descriptions unless explicitlystated or otherwise clear from the context. All such arrangements ofsoftware and/or hardware are intended to fall within the scope of thisdisclosure.

The block diagrams and flowchart illustrations depict methods,apparatus, systems, and computer program products. The elements andcombinations of elements in the block diagrams and flow diagrams, showfunctions, steps, or groups of steps of the methods, apparatus, systems,computer program products and/or computer-implemented methods. Any andall such functions-generally referred to herein as a “circuit,”“module,” or “system” may be implemented by computer programinstructions, by special-purpose hardware-based computer systems, bycombinations of special purpose hardware and computer instructions, bycombinations of general purpose hardware and computer instructions, andso on.

A programmable apparatus which executes any of the above-mentionedcomputer program products or computer-implemented methods may includeone or more microprocessors, microcontrollers, embeddedmicrocontrollers, programmable digital signal processors, programmabledevices, programmable gate arrays, programmable array logic, memorydevices, application specific integrated circuits, or the like. Each maybe suitably employed or configured to process computer programinstructions, execute computer logic, store computer data, and so on.

It will be understood that a computer may include a computer programproduct from a computer-readable storage medium and that this medium maybe internal or external, removable and replaceable, or fixed. Inaddition, a computer may include a Basic Input/Output System (BIOS),firmware, an operating system, a database, or the like that may include,interface with, or support the software and hardware described herein.

Embodiments of the present invention are limited to neither conventionalcomputer applications nor the programmable apparatus that run them. Toillustrate: the embodiments of the presently claimed invention couldinclude an optical computer, quantum computer, analog computer, or thelike. A computer program may be loaded onto a computer to produce aparticular machine that may perform any and all of the depictedfunctions. This particular machine provides a means for carrying out anyand all of the depicted functions.

Any combination of one or more computer readable media may be utilizedincluding but not limited to: a non-transitory computer readable mediumfor storage; an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor computer readable storage medium or anysuitable combination of the foregoing; a portable computer diskette; ahard disk; a random access memory (RAM); a read-only memory (ROM), anerasable programmable read-only memory (EPROM, Flash, MRAM, FeRAM, orphase change memory); an optical fiber; a portable compact disc; anoptical storage device; a magnetic storage device; or any suitablecombination of the foregoing. In the context of this document, acomputer readable storage medium may be any tangible medium that cancontain or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

It will be appreciated that computer program instructions may includecomputer executable code. A variety of languages for expressing computerprogram instructions may include without limitation C, C++, Java,JavaScript™, ActionScript™, assembly language, Lisp, Perl, Tcl, Python,Ruby, hardware description languages, database programming languages,functional programming languages, imperative programming languages, andso on. In embodiments, computer program instructions may be stored,compiled, or interpreted to run on a computer, a programmable dataprocessing apparatus, a heterogeneous combination of processors orprocessor architectures, and so on. Without limitation, embodiments ofthe present invention may take the form of web-based computer software,which includes client/server software, software-as-a-service,peer-to-peer software, or the like.

In embodiments, a computer may enable execution of computer programinstructions including multiple programs or threads. The multipleprograms or threads may be processed approximately simultaneously toenhance utilization of the processor and to facilitate substantiallysimultaneous functions. By way of implementation, any and all methods,program codes, program instructions, and the like described herein maybe implemented in one or more threads which may in turn spawn otherthreads, which may themselves have priorities associated with them. Insome embodiments, a computer may process these threads based on priorityor other order.

Unless explicitly stated or otherwise clear from the context, the verbs“execute” and “process” may be used interchangeably to indicate execute,process, interpret, compile, assemble, link, load, or a combination ofthe foregoing. Therefore, embodiments that execute or process computerprogram instructions, computer-executable code, or the like may act uponthe instructions or code in any and all of the ways described. Further,the method steps shown are intended to include any suitable method ofcausing one or more parties or entities to perform the steps. Theparties performing a step, or portion of a step, need not be locatedwithin a particular geographic location or country boundary. Forinstance, if an entity located within the United States causes a methodstep, or portion thereof, to be performed outside of the United Statesthen the method is considered to be performed in the United States byvirtue of the causal entity.

While the invention has been disclosed in connection with preferredembodiments shown and described in detail, various modifications andimprovements thereon will become apparent to those skilled in the art.Accordingly, the foregoing examples should not limit the spirit andscope of the present invention; rather it should be understood in thebroadest sense allowable by law.

What is claimed is:
 1. A computer-implemented method for videomanipulation comprising: loading content of a web page, wherein thecontent includes a frame for a plurality of short-form videos; analyzingthe content of the web page for textual information; accessing ashort-form video server; selecting a plurality of short-form videos fromthe short-form video server based on the textual information, whereinthe selecting includes automatically curating the plurality ofshort-form videos, wherein the automatic curating is based on a longshort-term memory model, wherein the long short-term memory modelprocesses one or more images, and wherein the long short-term memorymodel tracks recent behavior by a user accessing the web page, andwherein the automatic curating uses temporally weighted behaviors by auser accessing the web page, wherein the temporally weighted behaviorsinclude an amount of time spent on a web page; populating the frame onthe web page with the plurality of short-form videos obtained from theshort-form video server; and displaying representations of the pluralityof short-form videos within the frame on the web page.
 2. The method ofclaim 1 wherein the automatic curating comprises selecting, by a neuralnetwork, a subset of the plurality of short-form videos that areappropriate for the web page.
 3. The method of claim 2 wherein theautomatic curating comprises machine learning.
 4. The method of claim 2wherein the automatic curating provides the subset of videos based oncontextual information from the web page.
 5. The method of claim 4wherein the contextual information is based on natural languageprocessing for audio information from the web page.
 6. The method ofclaim 4 wherein the contextual information is based on video informationfrom the web page.
 7. The method of claim 1 wherein the frame is addedto an existing web page to facilitate display of relevant short-formvideos.
 8. The method of claim 1 wherein the frame on the web page pullsthe plurality of short-form videos from the short-form video server. 9.The method of claim 1 further comprising augmenting the textualinformation with metadata and performing the selecting the plurality ofshort-form videos based on the metadata.
 10. The method of claim 9further comprising augmenting the textual information with imageinformation and performing the selecting the plurality of short-formvideos based on the image information.
 11. The method of claim 9 furthercomprising augmenting the textual information with themes extracted fromthe web page and wherein the selecting is based on ranking associationsof metadata with short-form video theme information.
 12. The method ofclaim 1 further comprising auto playing the plurality of short-formvideos within the frame.
 13. The method of claim 1 further comprisingreceiving a response to a call to action embedded within the frame onthe web page.
 14. The method of claim 13 further comprising providing asecond plurality of short-form videos, based on the response to the callto action, from the short-form video server wherein criteria for thesecond plurality of short-form videos modifies the automatic curating ofthe plurality of short-form videos.
 15. The method of claim 1 furthercomprising using adaptive learning for the selecting, based on a user'sweb page behavior.
 16. The method of claim 15 wherein the adaptivelearning includes collecting the user's web page behavior before theselecting.
 17. The method of claim 15 wherein the adaptive learningincludes collecting the user's swipes and/or clicks on the web page. 18.The method of claim 17 wherein the adaptive learning includes collectingthe user's swipes and/or clicks on web pages related to the web page.19. The method of claim 15 wherein the adaptive learning comprisescomparing other users' historical patterns of web page behavior with theuser's web page behavior.
 20. The method of claim 15 wherein theadaptive learning combines the user's web page behavior with contextualinformation from the web page.
 21. The method of claim 15 wherein theadaptive learning includes demographic data about the user.
 22. Themethod of claim 15 further comprising an opting-in by the user tocollect additional web page behavior, and compensating the user for theopting-in.
 23. The method of claim 22 wherein the additional web pagebehavior includes monitoring the user's web page behavior on additionalwebsites.
 24. The method of claim 22, wherein the compensation includesaccess to short-form videos.
 25. The method of claim 22, wherein thecompensation includes crediting a blockchain coupon.
 26. A computerprogram product embodied in a non-transitory computer readable mediumfor video manipulation, the computer program product comprising codewhich causes one or more processors to perform operations of: loadingcontent of a web page, wherein the content includes a frame for aplurality of short-form videos; analyzing the content of the web pagefor textual information; accessing a short-form video server; selectinga plurality of short-form videos from the short-form video server basedon the textual information, wherein the selecting includes automaticallycurating the plurality of short-form videos, wherein the automaticcurating is based on a long short-term memory model, wherein the longshort-term memory model processes one or more images, and wherein thelong short-term memory model tracks recent behavior by a user accessingthe web page, and wherein the automatic curating uses temporallyweighted behaviors by a user accessing the web page, wherein thetemporally weighted behaviors include an amount of time spent on a webpage; populating the frame on the web page with the plurality ofshort-form videos obtained from the short-form video server; anddisplaying representations of the plurality of short-form videos withinthe frame on the web page.
 27. A computer system for video manipulationcomprising: a memory which stores instructions; one or more processorsattached to the memory, wherein the one or more processors, whenexecuting the instructions which are stored, are configured to: loadcontent of a web page, wherein the content includes a frame for aplurality of short-form videos; analyze the content of the web page fortextual information; access a short-form video server; select aplurality of short-form videos from the short-form video server based onthe textual information, wherein the selecting includes automaticallycurating the plurality of short-form videos, wherein the automaticcurating is based on a long short-term memory model, wherein the longshort-term memory model processes one or more images, and wherein thelong short-term memory model tracks recent behavior by a user accessingthe web page, and wherein the automatic curating uses temporallyweighted behaviors by a user accessing the web page, wherein thetemporally weighted behaviors include an amount of time spent on a webpage; populate the frame on the web page with the plurality ofshort-form videos obtained from the short-form video server; and displayrepresentations of the plurality of short-form videos within the frameon the web page.